Comparing SF-36® scores versus biomarkers to predict mortality in primary cardiac prevention patients


      • The practice of effective preventive medicine relies on adequate risk stratification.
      • SF-36®, a simple questionnaire completed by patients, outperforms biomarkers in predicting mortality in a primary cardiac prevention clinic in this exploratory study.
      • Additional data are needed to further define which specific patients might benefit from a questionnaire-based approach for risk stratification.



      Risk stratification plays an important role in evaluating patients with no known cardiovascular disease (CVD). Few studies have investigated health-related quality of life questionnaires such as the Medical Outcomes Study Short Form-36 (SF-36®) as predictive tools for mortality, particularly in direct comparison with biomarkers. Our objective is to measure the relative effectiveness of SF-36® scores in predicting mortality when compared to traditional and novel biomarkers in a primary prevention population.


      7056 patients evaluated for primary cardiac prevention between January 1996 and April 2011 were included in this study. Patient characteristics included medical history, SF-36® questionnaire and a laboratory panel (total cholesterol, triglycerides, HDL, LDL, ApoA, ApoB, ApoA1/ApoB ratio, homocysteine, lipoprotein (a), fibrinogen, hsCRP, uric acid and urine ACR). The primary outcome was all-cause mortality.


      A low SF-36® physical score independently predicted a 6-fold increase in death at 8 years (above vs. below median Hazard Ratio [95% confidence interval] 5.99 [3.86–9.35], p < 0.001). In a univariate analysis, SF-36® physical score had a c-index of 0.75, which was superior to that of all the biomarkers. It also carried incremental predictive ability when added to non-laboratory risk factors (Net Reclassification Index = 59.9%), as well as Framingham risk score components (Net Reclassification Index = 61.1%). Biomarkers added no incremental predictive value to a non-laboratory risk factor model when combined to SF-36 physical score.


      The SF-36® physical score is a reliable predictor of mortality in patients without CVD, and outperformed most studied traditional and novel biomarkers. In an era of rising healthcare costs, the SF-36® questionnaire could be used as an adjunct simple and cost-effective predictor of mortality to current predictors.


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        • Wang T.J.
        • Gona P.
        • Larson M.G.
        • Tofler G.H.
        • Levy D.
        • Newton-Cheh C.
        • et al.
        Multiple biomarkers for the prediction of first major cardiovascular events and death.
        N. Engl. J. Med. 2006; 355: 2631-2639
        • Melander O.
        • Newton-Cheh C.
        • Almgren P.
        • Hedblad B.
        • Berglund G.
        • Engstrom G.
        • et al.
        Novel and conventional biomarkers for prediction of incident cardiovascular events in the community.
        JAMA. 2009; 302: 49-57
        • Orszag P.R.
        • Ellis P.
        The challenge of rising health care costs--a view from the Congressional Budget Office.
        N. Engl. J. Med. 2007; 357: 1793-1795
        • Bodenheimer T.
        High and rising health care costs. Part 2: technologic innovation.
        Ann. Intern. Med. 2005; 142: 932-937
        • Lippi G.
        • Salvagno G.L.
        • Targher G.
        • Guidi G.C.
        Multiple biomarkers for the prediction of first major cardiovascular events and death: considerable costs and limited benefits.
        MedGenMed. 2007; 9: 34
        • Ware Jr., J.E.
        • Sherbourne C.D.
        The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.
        Med. Care. 1992; 30: 473-483
        • McHorney C.A.
        • Ware Jr., J.E.
        • Raczek A.E.
        The MOS 36-item short-form health survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs.
        Med. Care. 1993; 31: 247-263
        • Nilsson E.
        • Wenemark M.
        • Bendtsen P.
        • Kristenson M.
        Respondent satisfaction regarding SF-36 and EQ-5D, and patients' perspectives concerning health outcome assessment within routine health care.
        Qual. Life Res. 2007; 16: 1647-1654
        • Zhang J.P.
        • Pozuelo L.
        • Brennan D.M.
        • Hoar B.
        • Hoogwerf B.J.
        Association of SF-36 with coronary artery disease risk factors and mortality: a PreCIS study.
        Prev. Cardiol. 2010; 13: 122-129
        • Tippins R.B.
        • Torres W.E.
        • Baumgartner B.R.
        • Baumgarten D.A.
        Are screening serum creatinine levels necessary prior to outpatient CT examinations?.
        Radiology. 2000; 216: 481-484
        • Ranucci M.
        • Castelvecchio S.
        • Menicanti L.
        • Frigiola A.
        • Pelissero G.
        Risk of assessing mortality risk in elective cardiac operations: age, creatinine, ejection fraction, and the law of parsimony.
        Circulation. 2009; 119: 3053-3061
        • Wright Jr., J.T.
        • Williamson J.D.
        • Whelton P.K.
        • Snyder J.K.
        • Sink K.M.
        • Rocco M.V.
        • et al.
        A randomized trial of intensive versus standard blood-pressure control.
        N. Engl. J. Med. 2015; 373: 2103-2116
        • Blackstone E.H.
        Demise of a vital resource.
        J. Thorac. Cardiovasc. Surg. 2012; 143: 37-38
        • McHorney C.A.
        • Ware Jr, J.E.
        • Lu J.F.
        • Sherbourne C.D.
        The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups.
        • Ware Jr., J.E.
        SF-36 health survey update.
        in: Spine (Phila Pa 1976). 25. 2000: 3130-3139
        • Harrell F.E.
        • Lee K.L.
        • Mark D.B.
        Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.
        Stat. Med. 1996; 15: 361-387
        • Pencina M.J.
        • D'Agostino Sr., R.B.
        • Steyerberg E.W.
        Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.
        Stat. Med. 2011; 30: 11-21
        • Rumsfeld J.S.
        Health status and clinical practice: when will they meet?.
        Circulation. 2002; 106: 5-7
        • Calkins D.R.
        • Rubenstein L.V.
        • Cleary P.D.
        • Davies A.R.
        • Jette A.M.
        • Fink A.
        • et al.
        Failure of physicians to recognize functional disability in ambulatory patients.
        Ann. Intern. Med. 1991; 114: 451-454
        • McClellan W.M.
        • Anson C.
        • Birkeli K.
        • Tuttle E.
        Functional status and quality of life: predictors of early mortality among patients entering treatment for end stage renal disease.
        J. Clin. Epidemiol. 1991; 44: 83-89
        • Rumsfeld J.S.
        • MaWhinney S.
        • McCarthy Jr., M.
        • Shroyer A.L.
        • VillaNueva C.B.
        • O'Brien M.
        • et al.
        Health-related quality of life as a predictor of mortality following coronary artery bypass graft surgery. Participants of the Department of Veterans Affairs Cooperative Study Group on Processes, Structures, and Outcomes of Care in Cardiac Surgery.
        JAMA. 1999; 281: 1298-1303
        • CA O, SL R, Cherniack M.
        How does the SF-36 perform in healthy populations? A structured review of longitudinal studies.
        J Social Behav Health Sci. 2010; 1: 1-18
        • Hemingway H.
        • Stafford M.
        • Stansfeld S.
        • Shipley M.
        • Marmot M.
        Is the SF-36 a valid measure of change in population health? Results from the Whitehall II study.
        Br. Med. J. 1997; 315: 1273-1279
        • Rodriguez-Artalejo F.
        • Guallar-Castillon P.
        • Pascual C.R.
        • Otero C.M.
        • Montes A.O.
        • Garcia A.N.
        • et al.
        Health-related quality of life as a predictor of hospital readmission and death among patients with heart failure.
        Arch. Intern. Med. 2005; 165: 1274-1279
        • Lacson Jr., E.
        • Xu J.
        • Lin S.F.
        • Dean S.G.
        • Lazarus J.M.
        • Hakim R.M.
        A comparison of SF-36 and SF-12 composite scores and subsequent hospitalization and mortality risks in long-term dialysis patients.
        Clin. J. Am. Soc. Nephrol. 2010; 5: 252-260
        • Cuijpers P.
        • Smit F.
        Excess mortality in depression: a meta-analysis of community studies.
        J. Affect. Disord. 2002; 72: 227-236
        • Theou O.
        • Brothers T.D.
        • Mitnitski A.
        • Rockwood K.
        Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality.
        J. Am. Geriatr. Soc. 2013; 61: 1537-1551
        • Ensrud K.E.
        • Ewing S.K.
        • Cawthon P.M.
        • Fink H.A.
        • Taylor B.C.
        • Cauley J.A.
        • et al.
        A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men.
        J. Am. Geriatr. Soc. 2009; 57: 492-498
        • Fried L.P.
        • Tangen C.M.
        • Walston J.
        • Newman A.B.
        • Hirsch C.
        • Gottdiener J.
        • et al.
        Frailty in older adults: evidence for a phenotype.
        J Gerontol a-Biol. 2001; 56 (M146-M56)
        • Rothman M.D.
        • Leo-Summers L.
        • Gill T.M.
        Prognostic significance of potential frailty criteria.
        J. Am. Geriatr. Soc. 2008; 56: 2211-2216
        • Lopez O.G.
        • Bedoya A.D.
        • Gutierrez A.J.
        • Postigo S.B.
        Relationship between short-form health SF36 questionnaire and oxygen uptake in healthy workers.
        J Sport Med. Phys. Fitness. 2016; 56: 280-286
        • Harber M.P.
        • Kaminsky L.A.
        • Arena R.
        • Blair S.N.
        • Franklin B.A.
        • Myers J.
        • et al.
        Impact of cardiorespiratory fitness on all-cause and disease-specific mortality: advances since 2009.
        Prog. Cardiovasc. Dis. 2017;
        • Mahmoodi B.K.
        • Matsushita K.
        • Woodward M.
        • Blankestijn P.J.
        • Cirillo M.
        • Ohkubo T.
        • et al.
        Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without hypertension: a meta-analysis.
        Lancet. 2012; 380: 1649-1661
        • Peralta C.A.
        • Shlipak M.G.
        • Judd S.
        • Cushman M.
        • McClellan W.
        • Zakai N.A.
        • et al.
        Detection of chronic kidney disease with creatinine, cystatin C, and urine albumin-to-creatinine ratio and association with progression to end-stage renal disease and mortality.
        JAMA. 2011; 305: 1545-1552
        • Sarnak M.J.
        • Katz R.
        • Newman A.
        • Harris T.
        • Peralta C.A.
        • Devarajan P.
        • et al.
        Association of urinary injury biomarkers with mortality and cardiovascular events.
        J. Am. Soc. Nephrol. 2014; 25: 1545-1553
        • Yousuf O.
        • Mohanty B.D.
        • Martin S.S.
        • Joshi P.H.
        • Blaha M.J.
        • Nasir K.
        • et al.
        High-sensitivity C-reactive protein and cardiovascular disease: a resolute belief or an elusive link?.
        J. Am. Coll. Cardiol. 2013; 62: 397-408
        • Lloyd-Jones D.M.
        • Liu K.
        • Tian L.
        • Greenland P.
        Narrative review: assessment of C-reactive protein in risk prediction for cardiovascular disease.
        Ann. Intern. Med. 2006; 145: 35-42
        • Kaptoge S.
        • Di Angelantonio E.
        • Pennells L.
        • Wood A.M.
        • White I.R.
        • Gao P.
        • et al.
        C-reactive protein, fibrinogen, and cardiovascular disease prediction.
        N. Engl. J. Med. 2012; 367: 1310-1320
        • Kuller L.H.
        • Tracy R.P.
        • Shaten J.
        • Meilahn E.N.
        Relation of C-reactive protein and coronary heart disease in the MRFIT nested case-control study. Multiple Risk Factor Intervention Trial.
        Am. J. Epidemiol. 1996; 144: 537-547
        • Failde I.
        • Medina P.
        • Ramirez C.
        • Arana R.
        Assessing health-related quality of life among coronary patients: SF-36 vs SF-12.
        Public Health. 2009; 123: 615-617
        • Lam E.T.
        • Lam C.L.
        • Fong D.Y.
        • Huang W.W.
        Is the SF-12 version 2 Health Survey a valid and equivalent substitute for the SF-36 version 2 Health Survey for the Chinese?.
        J. Eval. Clin. Pract. 2013; 19: 200-208
        • Tucker G.
        • Adams R.
        • Wilson D.
        New Australian population scoring coefficients for the old version of the SF-36 and SF-12 health status questionnaires.
        Qual. Life Res. 2010; 19: 1069-1076
        • Johnson N.B.
        CDC National Health Report: leading causes of morbidity and mortality and associated behavioral risk and protective factors-United States, 2005–2013 (vol 63, pg 3, 2014).
        in: MMWR-Morbid Mortal W. 63. 2014: 1015